UC San Diego

The smart grid is driven by distributed, heterogeneous energy sources and controllable loads matched to sources to optimize energy usage. Ubiquitous data collection of energy generation and consumption is used for prediction of sources and loads, which in turn is used for net metering, and controlling retail energy prices and loads. This thesis proposes using context — additional high-level information — about elements of the smart grid (sources, loads, and storage) to improve the efficiency of its operation.

We first investigate the integration of local renewables in data centers, some of the largest consumers in the grid. We mitigate the variability of renewables with short-term prediction of solar and wind energy using additional environmental data. This allows us to use renewable energy for computing loads, the largest segment of energy consumption while maintaining quality of service requirements of the data center. We extend this work to a networked set of data centers, allocating and migrating jobs to improve energy efficiency.

While individual large consumers are of particular importance to the grid, there is relatively little focus on automated regulation of smaller consumers like individual houses, despite the residential sector consuming over 1/3 of national energy consumption. We propose a residential electrical energy simulation platform that enables investigating the impact of technologies such as renewable energy, different battery types, centralized vs. distributed in-home energy storage, and smart appliances.

While batteries are typically used as a buffer against inconsistent output from renewables, the growing deployment of reverse-power-operation systems provides residences with the ability to profit from buying and selling energy at the retail time-of-use rate. We develop a formulation for battery usage based on more realistic battery models, optimizing the benefit of discharging the battery and minimizing the time to return on investment.

Finally, unlike data centers, residential energy is directly driven by the activities and behavior of the users. The growing advent of sensing in the IoT presents a unique opportunity: using general-purpose modular data processing to generate grid-related context: energy prediction and flexibility, which can be used to drive individualized automated actuation that scales to the size of the grid.